But, Does it Matter?
1. MOTIVATION
Supply Chain Decision Making to Reduce the Manufacturing Greenhouse Gas Emissions of Solar Energy
2. LIFE CYCLE ASSESSMENT 3. METRICS 4. DECISION MAKING
Corinne Reich-Weiser Advisor: David Dornfeld
5. FUTURE WORK
Qualifying Exam April 25, 2008
Laboratory for Manufacturing and Sustainability
2
Renewable Energy
Are these improvements? Use LCA…
Wind
Material Extraction
Hydro
Drivers:
Material Processing
Energy Independence Health Concerns Climate Change
http://www.jamaicancaves.org
http://www.tourist-info-vianden.lu/images/page043.jpg
Manufacturing
http://www.danishexporters.dk/Grafik/danishexporters/wind _billede.jpg
Photovoltaics
http://www.energysolutionscenter.org
Solar Thermal
Use
End of Life
http://www.jamd.com
http://en.wikipedia.org
Recycle
Remanufacture
Reuse http://www.castlereagh.gov.uk
http://news.thomasnet.com/IMT/archives/2007/07/the_state_of_solar_power_energy_alternati ve_technology_flaws_potential.html
http://www.intuser.net/5/1/pictures/image027.jpg
3
Renewables versus Traditional Energy Technology
GHG Intensity
Data Sources
Renewables versus Traditional Energy
Reference
Technology
GHG Intensity
(g CO2eq/kWh) [Fthenakis et al., 2008]
Silicon
Australia EIOLCA
[Lenzen et al., 1999]
Solar Thermal
34.7 – 37.6
Boustead UK LCA Database
[Ardente et al., 2008]
Wind
8.8 – 18.5
20 – 40
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
Nuclear
Coal
900
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
Coal
Natural Gas
550
US EIOLCA
[Pacca et al., 2002]
Natural Gas
30 – 55
Solar Thermal
34.7 – 37.6
Wind
8.8 – 18.5
Nuclear
Data Sources
Reference
(g CO2eq/kWh) Ecoinvent for EU and Franklin for US
Silicon
What’s Missing?
Results DON’T enable supply chain decision making
Electricity Installation Variables Mix
Transit*
Packaging
Australia EIOLCA
[Lenzen et al., 1999]
Boustead UK LCA Database
[Ardente et al., 2008]
20 – 40
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
900
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
550
US EIOLCA
[Pacca et al., 2002]
But, Does it Matter?
Results1.2allow comparison on a 1 theoretical basis 0.8 0.6
5
What’s Missing? Electricity Installation Variables Mix
Shipping Losses
U.S. Average
0.4
Results0.2DON’T enable supply chain decision0 making y ta n ia k s n io a
Labor
www.aluminum.org http://en.wikipedia.org/wiki/Earth http://www.coloradocollege.edu/Dept/PC/RepresentativePhy/Pages/power.htm www.woodenpackaging.co.uk www.art-iceland.com www.oxfam.org.au
[Fthenakis et al., 2008]
Greenhouse Gas Emissions in US States [EIA, 2002].
Shipping Losses
GWP (kg CO2eq/kWh)
Results allow comparison on a theoretical basis
Ecoinvent for EU and Franklin for US
30 – 55
Transit*
Packaging
Labor
a go rn or x ga h w ck o re o Y Te ichi O Io ntu Dak O alif ew M Ke rth C N No
www.aluminum.org http://en.wikipedia.org/wiki/Earth http://www.coloradocollege.edu/Dept/PC/RepresentativePhy/Pages/power.htm www.woodenpackaging.co.uk www.art-iceland.com www.oxfam.org.au
6
1
Global Supply Chain
Aluminum Mfg Optics Mfg Semiconductor Mfg Glass Mfg
Global Supply Chain
GHG Impacts in U.S.: 30% of GHG from Power Generation 10% of GHG from Transportation [Carnegie Mellon, eiolca.net, 2008]
GHG Intensity of Electricity U.S.: 600 g CO2eq/kWh Diesel: 800 g CO2eq/kWh Coal: 900 g CO2eq/kWh Wind: 020 g CO2eq/kWh
Aluminum Mfg Optics Mfg Semiconductor Mfg Glass Mfg
GHG Intensity of Electricity U.S.: 600 g CO2eq/kWh Diesel: 800 g CO2eq/kWh Coal: 900 g CO2eq/kWh Wind: 020 g CO2eq/kWh
GHG Impacts in U.S.: 30% of GHG from Power Generation 10% of GHG from Transportation [Carnegie Mellon, eiolca.net, 2008]
SolFocus Case Study
SolFocus Case Study
Global Industrial Sector: - 40% of Electricity Use - 70% from Fossil Fuels [EIA, 2005]
Mountain View
Mountain View
Sunnyvale Suzhou
Sunnyvale Suzhou
Mesa
Madrid
Mesa
Madrid New Delhi
New Delhi 7
[S. Horne, SolFocus, Inc.]
8
[S. Horne, SolFocus, Inc.]
Thesis Life Cycle Assessment
Problem Definition
Metrics Development
1. MOTIVATION 2. LIFE CYCLE ASSESSMENT
Are GHG emissions reductions possible with strategic supply chain decision making?
3. METRICS 4. DECISION MAKING
Decision Making Tool
5. FUTURE WORK
Supply Chain Parameters Transportation; Electricity Mix; Scrap; Installation Variables 9
LCA Methodology
Life Cycle Assessment for Decision Making Hybrid LCA
Economic Input-Output LCA
Hybrid LCA
Carnegie Mellon University
Environmental Matrix
&
(EPA, DOE, etc.)
Î
Installation
Logging Chemicals
Furniture Mfg
Mfg. Component
Steel
Fasteners Mfg
EIOLCA
Plastics
Packaging Data
Purchased Service
Process LCA
Hybrid LCA
Society of Environmental Toxicology and Chemistry & EPA Electricity
Landfill
Component A
G
D
C
K
H
L
I
M
J
E
F
Packaging Data
Packaging Data
Process Data
Process Data
Machinery Data
Machinery Data
EIOLCA
EIOLCA
Mfg. Component
Evolves with Data Availability Establish Supply ChainAdvantages: Possibilities
Bullard, Hendrickson, Horvath, Joshi, Moriguchi, Suh, etc.
Tracker
For Example: Disadvantages: Missing Data, Price Estimation (China) Manufacturing Process Data EIOLCA
Hybrid LCA Transportation
Installation (Germany)
Component A Component B
Process Data
Transportation
EIOLCA
Example Wood Finishing
Economic InputOutput Table (BEA – 485 sectors)
10
Component B
EIOLCA
Solar Installation
Tracker Tracker (Japan)
Installation (USA)
PV (USA)
EIOLCA
Process Data
Electricity
EIOLCA
Labor
EIOLCA
Emissions
PV (India)
Photovoltaic
Panel (USA)
Panel
11
Panel (Germany)
Packaging
Packaging (Mexico) Packaging (USA)
12
2
SolFocus Data Structure Manufacturing Process Data Purchased Good/Service - EIOLCA Hybrid LCA Transit Included Transit Not Included
tracker steel
panel assembly
tracker controller
transformer
R&D
1. MOTIVATION
Administration
Final Installation
O&M
Overhead
2. LIFE CYCLE ASSESSMENT BOS wiring
inverter
concrete
rebar
other
labor
3. METRICS
primary secondary window backpan receiver
other
labor
electricity
4. DECISION MAKING
labor electricity
heat sink
process consumables glass
Ge wafer
cell
chip packaging
other
labor
electricity
5. FUTURE WORK
other
other coatings machine depreciation
[Reich-Weiser et al., 2008] 13
14
Previously Used Metrics
Energy Payback Time (EPBT)
Energy Payback Time (EPBT)
=
ELCA CE*ElecAnnualUseful
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
8
9
10
11
12
Lifetime*ESavedPerYear ELCA
Energy (MJ)
=
Lifetime EPBT
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
8
9
10
11
Lifetime EPBT
=
Lifetime*ESavedPerYear ELCA
=
ELCA = Primary Energy; GHGLCA = CO2eq Emissions; ESavedPerYear = Annual Offset Primary Energy; ElecAnnualUseful = Annual Electricity Output; CE = Conversion from Primary Energy to Electricity
15
Energy vs. GHG Intensity of Electricity GHG Emissions of Electricity
Primary Energy Consumption
Production Own Use Distribution
France
Disadvantages Assume U.S. or E.U. electricity conversion 1 Energy 2 3inappropriate 4 5 6for GHG 7 goal (hydro 9 8 10 vs. 12 11coal) Advantages Year
Understandable
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
9
8
10
11
12
Disadvantages Offset Scenario Ignored Does Not Promote Climate Change Mitigation Advantages Understandable; Tech. Comparisons
GHGLCA Lifetime*ElecAnnualUseful
ELCA = Primary Energy; GHGLCA = CO2eq Emissions; ESavedPerYear = Annual Offset Primary Energy; ElecAnnualUseful = Annual Electricity Output; CE = Conversion from Primary Energy to Electricity
16
Greenhouse Gas Return on Investment (GROI) Goal Definition 1. Goal/Concern
2. Metric Type
Scope Definition 3. Geographic Scope
4. Research Scope
Canada
Japan
Japan
Germany
Germany
United States
50 0 -50 -100 -150
GHG Emissions per kWh
Lifetime*ElecAnnualUseful
Canada
CE*ElecAnnualUseful
= 12
GHGLCA
France
ELCA
Energy Return on Investment (EROI)
GHG Emissions per kWh =
ESavedPerYear
=
Energy Return on Investment (EROI) =
ELCA
=
Energy (MJ)
ESavedPerYear
Energy (MJ)
ELCA
=
Energy (MJ)
Previously Used Metrics
United States
•Climate Change •Energy Independence •Toxic Emissions •Acid Rain •Smog •Etc…
•Impact •ROI •Non-Renewable •Renewable
•Supply Chain
•Global
•Factory •Local
•Machine
Australia
Australia
0 0.2 0.4 0.6 0.8 1 Electricity to GHG Conversion (kg CO2eq/kWh)
GHGElectricityMix =
GHGHeat&Electricity Electricity + η*Heat
0 2 4 6 8 10 12 Electricity to Energy Conversion (MJ/kWh) Advantages: Incorporates Own Use & Distribution Losses Disadvantages: Incomplete circularity (future work) Fuel supply chain not included
GHG Data: UNFCCC (2005), Electricity and Energy Data: IEA (2005), Circularity: OECD (1997/2002) GHGHeat&Electricity = CO2eq Emissions from Fossil Fuel Burning; Heat = Total Heat Production 17 η = Heat to Electricity Conversion Efficiency; Electricity = IEA Total Electricity Production;
GROI
GHGSavings GHGEmissions
=
Lifetime*ElecAnnualUseful*CGHG
GHGLCA
Advantages Accounts for Multiple Offset Scenarios Promotes Climate Change Mitigation better than EROI Disadvantages Not for comparing technologies
[Reich-Weiser et al., 2008]
Lifetime = Technology Lifetime; ElecAnnualUseful = Annual Electricity Output; CGHG = GHG Intensity of Offset Electricity Scenario; GHGLCA = LCA Determined CO2eq Emissions
18
3
GROI Offset Scenarios Scenario 1.
Scenario 2.
Scenario 3.
Scenario 4.
-Add Capacity -Centralized
-Add Capacity -Distributed.
-Replace Capacity -Centralized
-Replace Capacity -Distributed
1. MOTIVATION CGHG (GHG/kWh)
No
required to support new customers
for current electricity users
Yes
Yes
plugging into an existing electricity grid
No
2. LIFE CYCLE ASSESSMENT
Scenario 1, offset: Technology Lifecycle
3. METRICS
Scenario 2, offset: Distribution Losses* Technology Lifecycle
4. DECISION MAKING
No Yes Yes
plugging into an existing electricity grid
No
*Include distribution if alternative is grid-tied **Production Offset Could be Electricity Mix or a Component of the Mix ***Unconsidered by previous analysis
Scenario 3, offset: Production** Circularity*** Production Supply Chain
5. FUTURE WORK
Scenario 4, offset: Production** Circularity*** Distribution Losses Production Supply Chain [Reich-Weiser et al., 2008] 19
Green Supply Chain
Decision Making - Citing Locations Researcher
OPERATIONAL Production, Inventory, Processing, Scheduling
STRATEGIC Layout, Providers, Take-back, Re-use, Re-manufacturing
Facility Locations Minimize Transportation Weight and Distance
20
“Green” Suppliers Example: WalMart’s Packaging Scorecard
Objectives
Constraints Method Disadvantages
Zhou et al., 2000 Economic Sustainability [profit] Petrochemicals “Social” Sustainability [market demands] Resource Sustainability [unrecoverable materials, energy, capacity] Environmental Sustainability [hazardous waste, material recovered, energy recovered]
Available Materials LP
Weaver et al., 1997
Demand Capacity Flow balance (recycling and demand)
“Environmental impact”
Paper Recycling
- Weight Gain vs. Weight Loss Clarke et al., 2008
Transport Capacity AHP Weighted Inventory Capacity Metric
Requires input on desired values for each objective Weighting results in non-actionable results
Processing Capacity
LP
All locations equivalent
Weighted Metric
production, recycling, incineration, and transportation
Transportation Fate of Mfg. Toxins
Number of facilities Lagrange Relaxation Demand
Power differences not considered
Economics, GWP, Energy utilization, Fatalities
Freight Mode Hybrid LCA comparison Warehousing Freight forwarding
EIOLCA for all locations
Shoe re-man.
Facanha et al., 2005
- “Center of Gravity” http://www.lisasgraphicsandmore.com/freebies/soda_can.jpg http://valleybest-way.com/images/lumber.jpg
http://www.packaging-gateway.com/features/feature_images/pci022-wal-mart/3-scorecard.jpg
21
Logistics Outsourcing
22
Decision Making Future Work 1. MOTIVATION
Explore Supply Chain Optimization Schemes: C21β21
2. LIFE CYCLE ASSESSMENT
C11β11 D
3. METRICS
T11α111
Solar Installation
4. DECISION MAKING
Panel Assembly (Germany)
C12β12 T12α121
Panel Assembly (USA)
Window Glass (China)
T211α211
T212α212 T221α221
T222β222
5. FUTURE WORK Constraints: Demand Feasible Locations Capacity (transit, production) Inflow = Outflow*Yield 23
Assumptions: Steady State Material Availability Linear Relationships
C22β22
D = Total Demand Cij = Site Impact Tijk = Transportation Impact βij = units produced at site j αijk = units transported i = component of assembly j = site location
Window Glass (Canada)
Neglected: Lead Times Risk Personal Connections Flexibility Innovation 24
4
Future Work: Location Specific Data
Future Work - Water Goal Definition
Location Variables Quality Electricity Demand – Heating/Cooling Electricity Mix
•Climate Change •Water Scarcity •Toxic Emissions •Acid Rain •Smog •Etc…
Adjust EIOLCA Power Generation Data from U.S. to Country “A”: GHG $
GHG $
= A
_ US
GHGPG + $ US
GHGPG $ US
GHG kWh A GHG kWh US
Scope Definition 3. Geographic Scope
2. Goal Type
1. Goal/Concern
$1M in “Optical Instrument and Lens Manufacturing” Sector
•Supply Chain
•Global
•Impact •ROI •Non-Renewable •Renewable
4. Research Scope
•Factory •Local
•Machine
Water Consumption Factor:
(GHG/$)US (GHGPG/$)US
IrrigationLivestock 41%
Fraction Consumed (or Saved) WaterConsumptionMfg WCF = WaterAvailable - WaterConsumptionSociety
Industrial Mining 8% DomesticCommercial 12%
[eiolca.net screenshot]
Thermoelectric 39%
(GHG/$) = CO2eq per $ of production; GHGPG = Power Generation Component of (GHG/$); 25 (GHG/kWh)A = GHG/kWh of electricity used in Country A; (GHG/kWh)US = GHG/kWh used by EIOLCA
Research Summary
Local Metric for Supply Chain Decision Making? Use Maximum? Use Average? Data Requirements: Renewable Water (FAO), Water Consumption (FAO), Water of Electricity
U.S. Water Withdrawals [NREL, 2003]
[Reich-Weiser et al., 2008] 26
Research Summary
Thesis: Solar Manufacturing GHG emissions can be reduced through strategic supply chain design.
Problem
LCA
Metrics
In U.S. 40% of GHG Attributable to Supply Chain Decisions
Utilize Hybrid LCA
GROI:
Allow model to evolve with company and available info
Encourage climate change mitigation
Cost has dictated Mfg. occur in China and India using Coal and Diesel Electricity
Thesis: Solar Manufacturing GHG emissions can be reduced through strategic supply chain design.
Current Research
Future Work
Life Cycle Assessment
Life Cycle Assessment LOCATION SPECIFIC DATA
Hybrid LCA Methodology PACKAGING AND SHIPPING LOSSES
Metrics
Incorporate supply chain and installation variables
GREENHOUSE GAS RETURN ON INVESTMENT OFFSET SCENARIO METHODOLOGY
SCOPE PROTOTYPE
Excel Tool Review of Previous Work
Establish optimization method
Data Requirements
Data Requirements
Electricity Mix – GHG, Energy Transport CO2 and Energy Water Availability and Use
Country Water Use of Electricity Transportation Distances Electricity Mix – GHG, Energy (Circularity)
Case Studies
Case Study
SCOPE Prototype
WATER CONSUMPTION FACTOR
Tool and Decision Making
Tool and Decision Making
Are GHG emissions reductions possible with strategic supply chain decision making?
Uncertainty & Sensitivity Analysis
Metrics
SolFocus LCA Analysis
PV SUPPLY CHAIN SCENARIOS STORAGE & SOLAR THERMAL CASE STUDIES
Determine potential supply chains and make GROI tradeoffs 27
Research Schedule
Appendix - Design Space
Deliverables:
Product Design: Temporal
1) Methodology for incorporating GHG Supply Chain Tradeoffs: - Transportation, Electricity Mix, Scrap, Install Tradeoffs 2) Detailed Case Study of SolFocus Concentrator PV documenting Supply Chain GHG Reductions 3) SCOPE Tool Prototype Summer 2008
28
Fall 2008
Spring 2009
Summer 2009
Needs Definition
Fall 2009
Location Specific LCA Data
Conceptual Design
Detailed Design & Prototyping
Manufacturing & Testing
Process Parameter Adjustments
Post-Processing
Manufacturing Design: Temporal
Water Data & Metrics
Product & Process Design
Optimization Method Exploration PV Supply Chain Case Study
Process Design & Planning
Inclusion of Error SCOPE Tool Development
Manufacturing Design: Physical
Solar Thermal Case Study
Supply Chain
Thesis Writing
Laboratory for Manufacturing and Sustainability
29
Factory
Machinery
Tooling
30
5
Supply Chain Example
Example: Automobile Assembly Location
Assumptions: (1) Material Extraction US Base: 1.5 kg CO2eq/kg material at 30% electricity (2) Processing US Base: 0.5 kg CO2eq/kg at 100% electricity (3) Transportation approximated as 0.0001 kg CO2eq/kgkm (high for shipping, low for trucking) (4) 1kg of material transported and processed at each stage
3.7 kg CO2eq
Paris, France
Beijing, China 0.21 kg CO2eq
California, USA
1.3 kg CO2eq Tokyo, Japan
(2103 km)
3.3 kg CO2eq
1.1 kg CO2eq 0.97 kg CO2eq
0.3 kg CO2eq 0.83 kg CO2eq (8286 km)
(9739 km)
Paris, France
0.8 0.6
U.S. Average
0.4 0.2
3.2 kg CO2eq
Savings from Local Assembly
Trucking Savings
3500
1
2500
Assemble Locally
1500
Assemble in Michigan
500 -500
0
-1500
O r Ca e g lif o n N or n ew ia Yo T rk M e xa ic s hi ga n O hi o K Iow N en a or tu th c D ky ak ot a
0.95 kg CO2eq (9526 km)
1.2 GWP (kg CO2eq)
0.8 kg CO2eq
4500
GWP (kg CO2eq/kWh)
Total GHG Emissions
1.1 kg CO2eq 0.83 kg CO2eq (8238 km)
Mfg Tradeoffs – Assume local installation.
Greenhouse Gas Emissions in US States [EIA, 2002].
-2500 California
Texas
Ohio
Kentucky
Tokyo, Japan 0 kg CO2eq
1.3 kg CO2eq
2.3 kg CO2eq
Tokyo, Japan
(0 km)
31
Appendix – Transportation
Circularity Discrepancies
Transportation CO2 Emissions
Transportation Energy 20.00
870
800
Energy (kJ/kg-km)
CO2 (mg/kg-km)
1000
600 400 200 17
67
118
Water Freight
15.90
16.00
Own Use [IEA, 2005]
Circularity [OECD, 1997]
Australia
1.09
1.09
United States
1.05
1.11
Germany
1.08
1.04
Japan
1.05
1.11
Canada
1.06
1.0
France
1.09
1.10
12.00
0 Rail
32
8.00 2.44
4.00 0.23
0.37
Rail
Water Freight
0.00
Trucking Air Freight
Trucking Air Freight
Energy Data [Spielmann et al., 2005]: water frieght, trucking and rail [US DOE, 2004]: air freight (other values compared with Spielmann well) CO2 Data [Corbett et al., 2003]: water freight [Facanha et al., 2006]: rail, trucking, and air freight
33
Appendix - Metrics Methodology Goal Definition Goal •Climate Change •Energy Independence •Toxic Emissions •Acid Rain •Smog •Etc…
Appendix – ISO 14040
Scope Definition
Goal Type
•Impact •ROI •Non-Renewable •Renewable
Geographic Scope
•Global
Research Scope
1a. Goal Definition
a) Define the Process
- audience: decision makers - application: solar energy technology
b) Data Collection
•Supply Chain •Factory
•Local
34
•Machine
1b. Scope Definition - system boundaries - assumptions - limitations - functional unit: kWh of electricity produced
Inputs (energy, materials) Ozone Depleting Substances Particulates
Goal/Metric Types
Volatile Organic Compounds
2. Inventory Analysis
Toxic Emissions
3. Impact Assessment
Greenhouse Gases
- impact of the inventory on health and environment
Solid and Liquid Waste
4. Interpretation - optimization & Metrics
35 [C. Reich-Weiser, A. Vijayaraghavan, D. Dornfeld, 2008]
Product
36
6
Supply Chain Decision Making to Reduce the Manufacturing Greenhouse Gas Emissions of Solar Energy
2. LIFE CYCLE ASSESSMENT 3. METRICS 4. DECISION MAKING
Corinne Reich-Weiser Advisor: David Dornfeld
5. FUTURE WORK
Qualifying Exam April 25, 2008
Laboratory for Manufacturing and Sustainability
2
Renewable Energy
Are these improvements? Use LCA…
Wind
Material Extraction
Hydro
Drivers:
Material Processing
Energy Independence Health Concerns Climate Change
http://www.jamaicancaves.org
http://www.tourist-info-vianden.lu/images/page043.jpg
Manufacturing
http://www.danishexporters.dk/Grafik/danishexporters/wind _billede.jpg
Photovoltaics
http://www.energysolutionscenter.org
Solar Thermal
Use
End of Life
http://www.jamd.com
http://en.wikipedia.org
Recycle
Remanufacture
Reuse http://www.castlereagh.gov.uk
http://news.thomasnet.com/IMT/archives/2007/07/the_state_of_solar_power_energy_alternati ve_technology_flaws_potential.html
http://www.intuser.net/5/1/pictures/image027.jpg
3
Renewables versus Traditional Energy Technology
GHG Intensity
Data Sources
Renewables versus Traditional Energy
Reference
Technology
GHG Intensity
(g CO2eq/kWh) [Fthenakis et al., 2008]
Silicon
Australia EIOLCA
[Lenzen et al., 1999]
Solar Thermal
34.7 – 37.6
Boustead UK LCA Database
[Ardente et al., 2008]
Wind
8.8 – 18.5
20 – 40
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
Nuclear
Coal
900
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
Coal
Natural Gas
550
US EIOLCA
[Pacca et al., 2002]
Natural Gas
30 – 55
Solar Thermal
34.7 – 37.6
Wind
8.8 – 18.5
Nuclear
Data Sources
Reference
(g CO2eq/kWh) Ecoinvent for EU and Franklin for US
Silicon
What’s Missing?
Results DON’T enable supply chain decision making
Electricity Installation Variables Mix
Transit*
Packaging
Australia EIOLCA
[Lenzen et al., 1999]
Boustead UK LCA Database
[Ardente et al., 2008]
20 – 40
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
900
Ecoinvent for EU and Franklin for US
[Alsema et al., 2006]
550
US EIOLCA
[Pacca et al., 2002]
But, Does it Matter?
Results1.2allow comparison on a 1 theoretical basis 0.8 0.6
5
What’s Missing? Electricity Installation Variables Mix
Shipping Losses
U.S. Average
0.4
Results0.2DON’T enable supply chain decision0 making y ta n ia k s n io a
Labor
www.aluminum.org http://en.wikipedia.org/wiki/Earth http://www.coloradocollege.edu/Dept/PC/RepresentativePhy/Pages/power.htm www.woodenpackaging.co.uk www.art-iceland.com www.oxfam.org.au
[Fthenakis et al., 2008]
Greenhouse Gas Emissions in US States [EIA, 2002].
Shipping Losses
GWP (kg CO2eq/kWh)
Results allow comparison on a theoretical basis
Ecoinvent for EU and Franklin for US
30 – 55
Transit*
Packaging
Labor
a go rn or x ga h w ck o re o Y Te ichi O Io ntu Dak O alif ew M Ke rth C N No
www.aluminum.org http://en.wikipedia.org/wiki/Earth http://www.coloradocollege.edu/Dept/PC/RepresentativePhy/Pages/power.htm www.woodenpackaging.co.uk www.art-iceland.com www.oxfam.org.au
6
1
Global Supply Chain
Aluminum Mfg Optics Mfg Semiconductor Mfg Glass Mfg
Global Supply Chain
GHG Impacts in U.S.: 30% of GHG from Power Generation 10% of GHG from Transportation [Carnegie Mellon, eiolca.net, 2008]
GHG Intensity of Electricity U.S.: 600 g CO2eq/kWh Diesel: 800 g CO2eq/kWh Coal: 900 g CO2eq/kWh Wind: 020 g CO2eq/kWh
Aluminum Mfg Optics Mfg Semiconductor Mfg Glass Mfg
GHG Intensity of Electricity U.S.: 600 g CO2eq/kWh Diesel: 800 g CO2eq/kWh Coal: 900 g CO2eq/kWh Wind: 020 g CO2eq/kWh
GHG Impacts in U.S.: 30% of GHG from Power Generation 10% of GHG from Transportation [Carnegie Mellon, eiolca.net, 2008]
SolFocus Case Study
SolFocus Case Study
Global Industrial Sector: - 40% of Electricity Use - 70% from Fossil Fuels [EIA, 2005]
Mountain View
Mountain View
Sunnyvale Suzhou
Sunnyvale Suzhou
Mesa
Madrid
Mesa
Madrid New Delhi
New Delhi 7
[S. Horne, SolFocus, Inc.]
8
[S. Horne, SolFocus, Inc.]
Thesis Life Cycle Assessment
Problem Definition
Metrics Development
1. MOTIVATION 2. LIFE CYCLE ASSESSMENT
Are GHG emissions reductions possible with strategic supply chain decision making?
3. METRICS 4. DECISION MAKING
Decision Making Tool
5. FUTURE WORK
Supply Chain Parameters Transportation; Electricity Mix; Scrap; Installation Variables 9
LCA Methodology
Life Cycle Assessment for Decision Making Hybrid LCA
Economic Input-Output LCA
Hybrid LCA
Carnegie Mellon University
Environmental Matrix
&
(EPA, DOE, etc.)
Î
Installation
Logging Chemicals
Furniture Mfg
Mfg. Component
Steel
Fasteners Mfg
EIOLCA
Plastics
Packaging Data
Purchased Service
Process LCA
Hybrid LCA
Society of Environmental Toxicology and Chemistry & EPA Electricity
Landfill
Component A
G
D
C
K
H
L
I
M
J
E
F
Packaging Data
Packaging Data
Process Data
Process Data
Machinery Data
Machinery Data
EIOLCA
EIOLCA
Mfg. Component
Evolves with Data Availability Establish Supply ChainAdvantages: Possibilities
Bullard, Hendrickson, Horvath, Joshi, Moriguchi, Suh, etc.
Tracker
For Example: Disadvantages: Missing Data, Price Estimation (China) Manufacturing Process Data EIOLCA
Hybrid LCA Transportation
Installation (Germany)
Component A Component B
Process Data
Transportation
EIOLCA
Example Wood Finishing
Economic InputOutput Table (BEA – 485 sectors)
10
Component B
EIOLCA
Solar Installation
Tracker Tracker (Japan)
Installation (USA)
PV (USA)
EIOLCA
Process Data
Electricity
EIOLCA
Labor
EIOLCA
Emissions
PV (India)
Photovoltaic
Panel (USA)
Panel
11
Panel (Germany)
Packaging
Packaging (Mexico) Packaging (USA)
12
2
SolFocus Data Structure Manufacturing Process Data Purchased Good/Service - EIOLCA Hybrid LCA Transit Included Transit Not Included
tracker steel
panel assembly
tracker controller
transformer
R&D
1. MOTIVATION
Administration
Final Installation
O&M
Overhead
2. LIFE CYCLE ASSESSMENT BOS wiring
inverter
concrete
rebar
other
labor
3. METRICS
primary secondary window backpan receiver
other
labor
electricity
4. DECISION MAKING
labor electricity
heat sink
process consumables glass
Ge wafer
cell
chip packaging
other
labor
electricity
5. FUTURE WORK
other
other coatings machine depreciation
[Reich-Weiser et al., 2008] 13
14
Previously Used Metrics
Energy Payback Time (EPBT)
Energy Payback Time (EPBT)
=
ELCA CE*ElecAnnualUseful
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
8
9
10
11
12
Lifetime*ESavedPerYear ELCA
Energy (MJ)
=
Lifetime EPBT
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
8
9
10
11
Lifetime EPBT
=
Lifetime*ESavedPerYear ELCA
=
ELCA = Primary Energy; GHGLCA = CO2eq Emissions; ESavedPerYear = Annual Offset Primary Energy; ElecAnnualUseful = Annual Electricity Output; CE = Conversion from Primary Energy to Electricity
15
Energy vs. GHG Intensity of Electricity GHG Emissions of Electricity
Primary Energy Consumption
Production Own Use Distribution
France
Disadvantages Assume U.S. or E.U. electricity conversion 1 Energy 2 3inappropriate 4 5 6for GHG 7 goal (hydro 9 8 10 vs. 12 11coal) Advantages Year
Understandable
50 0 -50 -100 -150
1
2
3
4
5 6 Year
7
9
8
10
11
12
Disadvantages Offset Scenario Ignored Does Not Promote Climate Change Mitigation Advantages Understandable; Tech. Comparisons
GHGLCA Lifetime*ElecAnnualUseful
ELCA = Primary Energy; GHGLCA = CO2eq Emissions; ESavedPerYear = Annual Offset Primary Energy; ElecAnnualUseful = Annual Electricity Output; CE = Conversion from Primary Energy to Electricity
16
Greenhouse Gas Return on Investment (GROI) Goal Definition 1. Goal/Concern
2. Metric Type
Scope Definition 3. Geographic Scope
4. Research Scope
Canada
Japan
Japan
Germany
Germany
United States
50 0 -50 -100 -150
GHG Emissions per kWh
Lifetime*ElecAnnualUseful
Canada
CE*ElecAnnualUseful
= 12
GHGLCA
France
ELCA
Energy Return on Investment (EROI)
GHG Emissions per kWh =
ESavedPerYear
=
Energy Return on Investment (EROI) =
ELCA
=
Energy (MJ)
ESavedPerYear
Energy (MJ)
ELCA
=
Energy (MJ)
Previously Used Metrics
United States
•Climate Change •Energy Independence •Toxic Emissions •Acid Rain •Smog •Etc…
•Impact •ROI •Non-Renewable •Renewable
•Supply Chain
•Global
•Factory •Local
•Machine
Australia
Australia
0 0.2 0.4 0.6 0.8 1 Electricity to GHG Conversion (kg CO2eq/kWh)
GHGElectricityMix =
GHGHeat&Electricity Electricity + η*Heat
0 2 4 6 8 10 12 Electricity to Energy Conversion (MJ/kWh) Advantages: Incorporates Own Use & Distribution Losses Disadvantages: Incomplete circularity (future work) Fuel supply chain not included
GHG Data: UNFCCC (2005), Electricity and Energy Data: IEA (2005), Circularity: OECD (1997/2002) GHGHeat&Electricity = CO2eq Emissions from Fossil Fuel Burning; Heat = Total Heat Production 17 η = Heat to Electricity Conversion Efficiency; Electricity = IEA Total Electricity Production;
GROI
GHGSavings GHGEmissions
=
Lifetime*ElecAnnualUseful*CGHG
GHGLCA
Advantages Accounts for Multiple Offset Scenarios Promotes Climate Change Mitigation better than EROI Disadvantages Not for comparing technologies
[Reich-Weiser et al., 2008]
Lifetime = Technology Lifetime; ElecAnnualUseful = Annual Electricity Output; CGHG = GHG Intensity of Offset Electricity Scenario; GHGLCA = LCA Determined CO2eq Emissions
18
3
GROI Offset Scenarios Scenario 1.
Scenario 2.
Scenario 3.
Scenario 4.
-Add Capacity -Centralized
-Add Capacity -Distributed.
-Replace Capacity -Centralized
-Replace Capacity -Distributed
1. MOTIVATION CGHG (GHG/kWh)
No
required to support new customers
for current electricity users
Yes
Yes
plugging into an existing electricity grid
No
2. LIFE CYCLE ASSESSMENT
Scenario 1, offset: Technology Lifecycle
3. METRICS
Scenario 2, offset: Distribution Losses* Technology Lifecycle
4. DECISION MAKING
No Yes Yes
plugging into an existing electricity grid
No
*Include distribution if alternative is grid-tied **Production Offset Could be Electricity Mix or a Component of the Mix ***Unconsidered by previous analysis
Scenario 3, offset: Production** Circularity*** Production Supply Chain
5. FUTURE WORK
Scenario 4, offset: Production** Circularity*** Distribution Losses Production Supply Chain [Reich-Weiser et al., 2008] 19
Green Supply Chain
Decision Making - Citing Locations Researcher
OPERATIONAL Production, Inventory, Processing, Scheduling
STRATEGIC Layout, Providers, Take-back, Re-use, Re-manufacturing
Facility Locations Minimize Transportation Weight and Distance
20
“Green” Suppliers Example: WalMart’s Packaging Scorecard
Objectives
Constraints Method Disadvantages
Zhou et al., 2000 Economic Sustainability [profit] Petrochemicals “Social” Sustainability [market demands] Resource Sustainability [unrecoverable materials, energy, capacity] Environmental Sustainability [hazardous waste, material recovered, energy recovered]
Available Materials LP
Weaver et al., 1997
Demand Capacity Flow balance (recycling and demand)
“Environmental impact”
Paper Recycling
- Weight Gain vs. Weight Loss Clarke et al., 2008
Transport Capacity AHP Weighted Inventory Capacity Metric
Requires input on desired values for each objective Weighting results in non-actionable results
Processing Capacity
LP
All locations equivalent
Weighted Metric
production, recycling, incineration, and transportation
Transportation Fate of Mfg. Toxins
Number of facilities Lagrange Relaxation Demand
Power differences not considered
Economics, GWP, Energy utilization, Fatalities
Freight Mode Hybrid LCA comparison Warehousing Freight forwarding
EIOLCA for all locations
Shoe re-man.
Facanha et al., 2005
- “Center of Gravity” http://www.lisasgraphicsandmore.com/freebies/soda_can.jpg http://valleybest-way.com/images/lumber.jpg
http://www.packaging-gateway.com/features/feature_images/pci022-wal-mart/3-scorecard.jpg
21
Logistics Outsourcing
22
Decision Making Future Work 1. MOTIVATION
Explore Supply Chain Optimization Schemes: C21β21
2. LIFE CYCLE ASSESSMENT
C11β11 D
3. METRICS
T11α111
Solar Installation
4. DECISION MAKING
Panel Assembly (Germany)
C12β12 T12α121
Panel Assembly (USA)
Window Glass (China)
T211α211
T212α212 T221α221
T222β222
5. FUTURE WORK Constraints: Demand Feasible Locations Capacity (transit, production) Inflow = Outflow*Yield 23
Assumptions: Steady State Material Availability Linear Relationships
C22β22
D = Total Demand Cij = Site Impact Tijk = Transportation Impact βij = units produced at site j αijk = units transported i = component of assembly j = site location
Window Glass (Canada)
Neglected: Lead Times Risk Personal Connections Flexibility Innovation 24
4
Future Work: Location Specific Data
Future Work - Water Goal Definition
Location Variables Quality Electricity Demand – Heating/Cooling Electricity Mix
•Climate Change •Water Scarcity •Toxic Emissions •Acid Rain •Smog •Etc…
Adjust EIOLCA Power Generation Data from U.S. to Country “A”: GHG $
GHG $
= A
_ US
GHGPG + $ US
GHGPG $ US
GHG kWh A GHG kWh US
Scope Definition 3. Geographic Scope
2. Goal Type
1. Goal/Concern
$1M in “Optical Instrument and Lens Manufacturing” Sector
•Supply Chain
•Global
•Impact •ROI •Non-Renewable •Renewable
4. Research Scope
•Factory •Local
•Machine
Water Consumption Factor:
(GHG/$)US (GHGPG/$)US
IrrigationLivestock 41%
Fraction Consumed (or Saved) WaterConsumptionMfg WCF = WaterAvailable - WaterConsumptionSociety
Industrial Mining 8% DomesticCommercial 12%
[eiolca.net screenshot]
Thermoelectric 39%
(GHG/$) = CO2eq per $ of production; GHGPG = Power Generation Component of (GHG/$); 25 (GHG/kWh)A = GHG/kWh of electricity used in Country A; (GHG/kWh)US = GHG/kWh used by EIOLCA
Research Summary
Local Metric for Supply Chain Decision Making? Use Maximum? Use Average? Data Requirements: Renewable Water (FAO), Water Consumption (FAO), Water of Electricity
U.S. Water Withdrawals [NREL, 2003]
[Reich-Weiser et al., 2008] 26
Research Summary
Thesis: Solar Manufacturing GHG emissions can be reduced through strategic supply chain design.
Problem
LCA
Metrics
In U.S. 40% of GHG Attributable to Supply Chain Decisions
Utilize Hybrid LCA
GROI:
Allow model to evolve with company and available info
Encourage climate change mitigation
Cost has dictated Mfg. occur in China and India using Coal and Diesel Electricity
Thesis: Solar Manufacturing GHG emissions can be reduced through strategic supply chain design.
Current Research
Future Work
Life Cycle Assessment
Life Cycle Assessment LOCATION SPECIFIC DATA
Hybrid LCA Methodology PACKAGING AND SHIPPING LOSSES
Metrics
Incorporate supply chain and installation variables
GREENHOUSE GAS RETURN ON INVESTMENT OFFSET SCENARIO METHODOLOGY
SCOPE PROTOTYPE
Excel Tool Review of Previous Work
Establish optimization method
Data Requirements
Data Requirements
Electricity Mix – GHG, Energy Transport CO2 and Energy Water Availability and Use
Country Water Use of Electricity Transportation Distances Electricity Mix – GHG, Energy (Circularity)
Case Studies
Case Study
SCOPE Prototype
WATER CONSUMPTION FACTOR
Tool and Decision Making
Tool and Decision Making
Are GHG emissions reductions possible with strategic supply chain decision making?
Uncertainty & Sensitivity Analysis
Metrics
SolFocus LCA Analysis
PV SUPPLY CHAIN SCENARIOS STORAGE & SOLAR THERMAL CASE STUDIES
Determine potential supply chains and make GROI tradeoffs 27
Research Schedule
Appendix - Design Space
Deliverables:
Product Design: Temporal
1) Methodology for incorporating GHG Supply Chain Tradeoffs: - Transportation, Electricity Mix, Scrap, Install Tradeoffs 2) Detailed Case Study of SolFocus Concentrator PV documenting Supply Chain GHG Reductions 3) SCOPE Tool Prototype Summer 2008
28
Fall 2008
Spring 2009
Summer 2009
Needs Definition
Fall 2009
Location Specific LCA Data
Conceptual Design
Detailed Design & Prototyping
Manufacturing & Testing
Process Parameter Adjustments
Post-Processing
Manufacturing Design: Temporal
Water Data & Metrics
Product & Process Design
Optimization Method Exploration PV Supply Chain Case Study
Process Design & Planning
Inclusion of Error SCOPE Tool Development
Manufacturing Design: Physical
Solar Thermal Case Study
Supply Chain
Thesis Writing
Laboratory for Manufacturing and Sustainability
29
Factory
Machinery
Tooling
30
5
Supply Chain Example
Example: Automobile Assembly Location
Assumptions: (1) Material Extraction US Base: 1.5 kg CO2eq/kg material at 30% electricity (2) Processing US Base: 0.5 kg CO2eq/kg at 100% electricity (3) Transportation approximated as 0.0001 kg CO2eq/kgkm (high for shipping, low for trucking) (4) 1kg of material transported and processed at each stage
3.7 kg CO2eq
Paris, France
Beijing, China 0.21 kg CO2eq
California, USA
1.3 kg CO2eq Tokyo, Japan
(2103 km)
3.3 kg CO2eq
1.1 kg CO2eq 0.97 kg CO2eq
0.3 kg CO2eq 0.83 kg CO2eq (8286 km)
(9739 km)
Paris, France
0.8 0.6
U.S. Average
0.4 0.2
3.2 kg CO2eq
Savings from Local Assembly
Trucking Savings
3500
1
2500
Assemble Locally
1500
Assemble in Michigan
500 -500
0
-1500
O r Ca e g lif o n N or n ew ia Yo T rk M e xa ic s hi ga n O hi o K Iow N en a or tu th c D ky ak ot a
0.95 kg CO2eq (9526 km)
1.2 GWP (kg CO2eq)
0.8 kg CO2eq
4500
GWP (kg CO2eq/kWh)
Total GHG Emissions
1.1 kg CO2eq 0.83 kg CO2eq (8238 km)
Mfg Tradeoffs – Assume local installation.
Greenhouse Gas Emissions in US States [EIA, 2002].
-2500 California
Texas
Ohio
Kentucky
Tokyo, Japan 0 kg CO2eq
1.3 kg CO2eq
2.3 kg CO2eq
Tokyo, Japan
(0 km)
31
Appendix – Transportation
Circularity Discrepancies
Transportation CO2 Emissions
Transportation Energy 20.00
870
800
Energy (kJ/kg-km)
CO2 (mg/kg-km)
1000
600 400 200 17
67
118
Water Freight
15.90
16.00
Own Use [IEA, 2005]
Circularity [OECD, 1997]
Australia
1.09
1.09
United States
1.05
1.11
Germany
1.08
1.04
Japan
1.05
1.11
Canada
1.06
1.0
France
1.09
1.10
12.00
0 Rail
32
8.00 2.44
4.00 0.23
0.37
Rail
Water Freight
0.00
Trucking Air Freight
Trucking Air Freight
Energy Data [Spielmann et al., 2005]: water frieght, trucking and rail [US DOE, 2004]: air freight (other values compared with Spielmann well) CO2 Data [Corbett et al., 2003]: water freight [Facanha et al., 2006]: rail, trucking, and air freight
33
Appendix - Metrics Methodology Goal Definition Goal •Climate Change •Energy Independence •Toxic Emissions •Acid Rain •Smog •Etc…
Appendix – ISO 14040
Scope Definition
Goal Type
•Impact •ROI •Non-Renewable •Renewable
Geographic Scope
•Global
Research Scope
1a. Goal Definition
a) Define the Process
- audience: decision makers - application: solar energy technology
b) Data Collection
•Supply Chain •Factory
•Local
34
•Machine
1b. Scope Definition - system boundaries - assumptions - limitations - functional unit: kWh of electricity produced
Inputs (energy, materials) Ozone Depleting Substances Particulates
Goal/Metric Types
Volatile Organic Compounds
2. Inventory Analysis
Toxic Emissions
3. Impact Assessment
Greenhouse Gases
- impact of the inventory on health and environment
Solid and Liquid Waste
4. Interpretation - optimization & Metrics
35 [C. Reich-Weiser, A. Vijayaraghavan, D. Dornfeld, 2008]
Product
36
6
